System and method for efficient management of a search database for retrieving context-based information

ABSTRACT

A system and method for efficient management of a search database for retrieving context-based information, system including a database and processor, wherein the database includes columnar database for storing a plurality of documents, an ontological database configured to represent a plurality of concepts as nodes in a network and relationships between the concepts as edges between nodes and the search database configured to store an inverted index of the plurality of documents in the columnar database. Herein, the processor is configured to identify, using the ontological database, a set of concepts in each of the plurality of documents and store, in the search database, corresponding to a given document the set of concepts identified in the given document, and secondary concepts relating to the given document, wherein a secondary concept has a direct relationship in the network with at least one of the concepts in the set.

TECHNICAL FIELD

The present disclosure relates generally to management of database; andmore specifically, to systems and methods for efficient management ofsearch databases for retrieving context-based information.

BACKGROUND

In recent years, the data related to biomedical entities has grownexponentially. Additionally, the data has inter-hierarchical connectionsalong with intra-hierarchical connections. Furthermore, system leveldatabase functionalities comprise data dictionary management, datastorage management, data security management, backup and recoverymanagement, database communication interfaces, database access languagesand application programming interfaces, easy search Interface, attributerelationship Interface, graph ontology environment with version control,and so forth. Hence, a database management managing the data shouldserve all the system level database functionalities.

However, performing aggregation, search and insertion of data in asingle common platform results in heap issues and performancebottlenecks. Furthermore, proper integration of ontology and raw data isnot possible in existing databases. In order to perform a change in theontology and raw data, the whole raw data needs to be reinserted again.Subsequently, repeated inverted index is required for different searchtypes of functionality which often leads to duplication of data storagein indexes. Additionally, limited scalability is persistent in existingdatabases. Hence, small ontology is easily supported but as the ontologyis scaled, the cost of the server increases with respect to the scalingof ontology and the ontological database is not able to scale.Furthermore, indexing of data for search purposes is slow due to extraprocessing time consumed for tagging using ontology. Notably, using onlyone type of database engine to perform different databasefunctionalities create overhead in the cost of complexity.

Therefore, in light of the aforementioned drawbacks, there existsproblems associated with conventional data management methods.

SUMMARY

The present disclosure seeks to provide a system for efficientmanagement of a search database for retrieving context-basedinformation. The present disclosure also seeks to provide a method ofretrieving context-based information relating to a search query. An aimof the present disclosure is to provide a solution that overcomes atleast partially the problems encountered in prior art.

In one aspect, the present disclosure provides a system for efficientmanagement of a search database for retrieving context-basedinformation, the system comprising a database and a processor,

wherein the database comprises:

a columnar database for storing a plurality of documents;

an ontological database configured to represent a plurality of conceptsas nodes in a network and relationships between the concepts as edgesbetween the nodes; and

the search database configured to store an inverted index of theplurality of documents in the columnar database;

wherein the processor is configured to identify, using the ontologicaldatabase, a set of concepts in each of the plurality of documents andstore, in the search database, corresponding to a given document:

the set of concepts identified in the given document; and

secondary concepts relating to the given document, wherein a secondaryconcept has a direct relationship in the network with at least one ofthe concepts in the set.

In another aspect, the present disclosure provides a method ofretrieving context-based information relating to a search query, whereinthe method is implemented using a database, the database comprising:

a columnar database for storing a plurality of documents,

an ontological database configured to represent a plurality of conceptsas nodes in a network and relationships between the concepts as edgesbetween the nodes, and

a search database configured to store an inverted index of the pluralityof documents in the columnar database, wherein the inverted indexstores, corresponding to a given document a set of concepts identifiedin the given document, and secondary concepts relating to the givendocument, wherein a secondary concept has a direct relationship in thenetwork with at least one of the concepts in the set;

wherein the method comprises

receiving the search query;

identifying at least one concept relating to the search query, using theontological database; and

identifying at least one document, from the columnar database, relatingto the search query based on the at least one concept relating to thesearch query.

Embodiments of the present disclosure substantially eliminate or atleast to partially address the aforementioned problems in the prior art,and establishes communication and networking between the columnardatabase the ontological database and the search database with reducedoverhead and no increase in cost with complexity.

Additional aspects, advantages, features and objects of the presentdisclosure would be made apparent from the drawings and the detaileddescription of the illustrative embodiments construed in conjunctionwith the appended claims that follow.

It will be appreciated that features of the present disclosure aresusceptible to being combined in various combinations without departingfrom the scope of the present disclosure as defined by the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating the presentdisclosure, exemplary constructions of the disclosure are shown in thedrawings. However, the present disclosure is not limited to specificmethods and instrumentalities disclosed herein. Moreover, those skilledin the art will understand that the drawings are not to scale. Whereverpossible, like elements have been indicated by identical numbers.

Embodiments of the present disclosure will now be described, by way ofexample only, with reference to the following diagrams wherein:

FIG. 1 is a block diagram of a system for efficient management of asearch database for retrieving context-based information, in accordancewith an embodiment of the present disclosure; and

FIG. 2 is a flowchart depicting steps of a method of retrievingcontext-based information relating to a search query, in accordance withan embodiment of the present disclosure.

In the accompanying drawings, an underlined number is employed torepresent an item over which the underlined number is positioned or anitem to which the underlined number is adjacent. A non-underlined numberrelates to an item identified by a line linking the non-underlinednumber to the item. When a number is non-underlined and accompanied byan associated arrow, the non-underlined number is used to identify ageneral item at which the arrow is pointing.

DETAILED DESCRIPTION OF EMBODIMENTS

The following detailed description illustrates embodiments of thepresent disclosure and ways in which they can be implemented. Althoughsome modes of carrying out the present disclosure have been disclosed,those skilled in the art would recognize that other embodiments forcarrying out or practising the present disclosure are also possible.

In one aspect, the present disclosure provides a system for efficientmanagement of a search database for retrieving context-basedinformation, the system comprising a database and a processor,

wherein the database comprises:

a columnar database for storing a plurality of documents;

an ontological database configured to represent a plurality of conceptsas nodes in a network and relationships between the concepts as edgesbetween the nodes; and

the search database configured to store an inverted index of theplurality of documents in the columnar database;

wherein the processor is configured to identify, using the ontologicaldatabase, a set of concepts in each of the plurality of documents andstore, in the search database, corresponding to a given document:

the set of concepts identified in the given document; and

secondary concepts relating to the given document, wherein a secondaryconcept has a direct relationship in the network with at least one ofthe concepts in the set.

In another aspect, an embodiment of the present disclosure provides amethod of retrieving context-based information relating to a searchquery, wherein the method is implemented using a database, the databasecomprising:

a columnar database for storing a plurality of documents,

an ontological database configured to represent a plurality of conceptsas nodes in a network and relationships between the concepts as edgesbetween the nodes, and

a search database configured to store an inverted index of the pluralityof documents in the columnar database, wherein the inverted indexstores, to corresponding to a given document a set of conceptsidentified in the given document, and secondary concepts relating to thegiven document, wherein a secondary concept has a direct relationship inthe network with at least one of the concepts in the set;

wherein the method comprises

receiving the search query;

identifying at least one concept relating to the search query, using theontological database; and

identifying at least one document, from the columnar database, relatingto the search query based on the at least one concept relating to thesearch query.

The system and method of the present disclosure aim to provide a systemdescribed herein that may be employed for system level databasefunctionalities comprising data dictionary management, data storagemanagement, data security management, backup and recovery management,database communication interfaces, database access languages andapplication programming interfaces, easy search interface, attributerelationship interface, graph ontological database environment withversion control, and so forth. Furthermore, the present disclosureresolves heap issues and performance bottlenecks observed when a singlecommon platform performs aggregation, search and insertion of data.Additionally, the present disclosure ensures proper integration ofontological database and raw data. Notably, the whole raw data need notbe reinserted again in order to perform a change in ontological databaseand raw data. Subsequently, the present disclosure prevents duplicationof data storage in indexes while performing repeated inverted index fordifferent types of system level database functionalities. Additionally,the present disclosure resolves limited scalability persistent inexisting databases. Moreover, the cost of the server is economical asthe ontological database is scaled. Furthermore, the processing timerequired for tagging using ontological database while indexing of datafor search purposes is not time consuming. Notably, the presentdisclosure uses the columnar database, the ontological database and thesearch database to perform different database functionalities hencepreventing the creation of overhead in the cost of complexity.

Pursuant to the embodiments of the present disclosure the systemdescribed herein relates to the creation of a database system which hasthe practical implementation of basic life science data. Moreover, inrecent years, this data is growing exponentially. Furthermore, the datapossesses inter-hierarchical connection and intra-hierarchicalconnection. Additionally, the present disclosure enables system leveldatabase functionalities such as data storage management, data securitymanagement, backup and recovery management, database communicationinterfaces, database access languages and application programminginterfaces, easy search interface, and so forth. Hence, the presentdisclosure undertakes a systematic approach with precomputation andother preprocesses of raw data and provide with the final result.

Throughout the present disclosure, the term “database” as used hereinrelates to an organized body of digital information regardless of themanner in which the data or the organized body thereof is represented.Optionally, the database may be hardware, software, firmware and/or anycombination thereof. For example, the organized body of related data maybe in the form of a table, a map, a grid, a packet, a datagram, a file,a document, a list or in any other form. The database includes any datastorage software and systems. Optionally, the database may be operableto support relational operations, regardless of whether it enforcesstrict adherence to the relational model, as understood by those ofordinary skill in the art.

The database comprising a columnar database configured for storing aplurality of documents. Herein, the columnar database is used to storeterabytes of raw data which can support functionality of index lookupand persistent storage. Herein, the document is a description of anelectronic copy and may be in a form of a web page, internet document,multimedia file and so forth. Additionally, document may be a set ofinformation related to the biomedical entity, object and so forth.Herein, the raw data may be crawled and cleaned through a module whichinteracts with the database and is stored in the columnar database.

Throughout the present disclosure, the term “ontological database”relates to a database storing set of concepts (namely, information,ideas, data, semantic associations and so forth) that elaborate typesand properties of the set of concepts and semantic associationsestablished therebetween. Specifically, the ontological databaseprovides information on relations of certain concepts in a specificfield to one or more concepts in other fields. Furthermore, theontological database provides a base for extracting contextually(namely, conceptually) relevant information pertaining to the specificfield required by the user. Additionally, the ontological databaseprovides a structured, optimal and relevant set of concepts pertainingto the specific field required by the user. Moreover, developing theontological database provides significant outcome for conductingscientific research, academic studies, market analysis and so forth.Optionally, the ontological database may include concepts in form oftext, image, audio, video, or any combination thereof.

The database comprising an ontological database configured to representa plurality of concepts as nodes in the network and relationshipsbetween the concepts as edges between the nodes. Herein, the term“concept” refers to a keyword in a given domain that may be a singleword or a phrase. Furthermore, the concept may be a synonym, analternate form and/or other closely related terms generally usedinterchangeably with the preferred concept. Additionally, the conceptmaybe arranged hierarchically by subject categories with the morespecific concept arranged beneath the broader concept. Optionally, theontological database is used to store the Life Science ontologicaldatabase which has multiple nodes and edges. Specifically, theontological database may help in finding the relationship of topbiomedical entity concepts. Notably, the ontological database stored inhierarchical form and/or relationships. Subsequently, the hierarchicaland graphical knowledge is used to enhance the mechanism of informationretrieval and finding the relationship among top concepts. Additionally,it helps in insight generation from vast amount of information.

In an embodiment, the ontological database is configured to store theplurality of concepts related to a biomedical domain. The plurality ofconcepts may be biomedical entities. Herein, the term “biomedicalentities” refers to a therapeutic data unit related to biomedicalsciences. Notably, the biomedical entities have an associationtherebetween based on functional aspect thereof. For example, thebiomedical entity ‘Nexium’ may be used to reduce production of stomachacid in human body, wherein ‘stomach acid’ may be another biomedicalentity. Furthermore, the biomedical entities and associations thereofare analysed to determine diagnosis, monitoring and therapy of aspecific disease associated thereto. Additionally, the biomedicalentities are mapped with related one or more biomedical entities inorder to identify associations therebetween.

Optionally, the ontological database is configured to store,corresponding to each of the relationships between the concepts, aweightage score of the relationship. Herein, the weightage score of agiven relationship is indicative of an importance of the relationship.Notably, a relationship with a high weightage score between two conceptsrepresents that said two concepts are closely interlinked. The weightagescore of a relationship between two concepts may be determined based onfrequency of occurrence of said two concepts in the plurality ofdocuments. Notably, the ontological database is represented in a treenetwork topology as the network. Herein, the tree network topology is aspecial type of structure where many connected concepts are arrangedlike the branches of a tree. Furthermore, there can be only oneconnection between any two connected nodes.

In an embodiment, the database is developed in Golang which helps innetworking among the database engines for data sharing. Herein, Golangis a statically typed, compiled programming language that provides alightweight interface to a row-oriented database. Furthermore, thepresent disclosure is used for database recovery, maintainingconsistency, fast transfer of data, lookup, create, read, update, anddelete (CRUD) operations.

The database comprising a search database configured to store aninverted index of the plurality of documents in the columnar database.Herein, the raw data from the columnar database is analyzed using theontological database to identify to concepts in each of the plurality ofdocuments and the resultant data is indexed using the search database.Subsequently, the concepts are tagged and stored as an inverted index inthe search database. Notably, the tagging of the concepts is performedsimultaneously while moving the data from columnar database to thesearch database with the help of ontological database.

In an embodiment, the present disclosure performs auto scaling by thecolumnar database, the ontological database and the search databaseusing their own mechanism, wherein the database maintains a stablethroughput which each of the database can handle. Herein, throughput isdata per second travelling to the columnar database, the ontologicaldatabase and the search database. Furthermore, uniform database querylanguage module is written in the database which helps in interactionwith external users such as for CRUD, search operations and so forth.Optionally, a dashboard is developed which takes the input from thedatabase in real time to continuously monitor the health of the system.Subsequently, the logs of all the three databases are compiled andanalyzed by the columnar database, the ontological database and thesearch database and a common log gets generated, which can be visible inthe dashboards in a better visualization.

Throughout the present disclosure, the term “processor” may include, butis not limited to, a microprocessor, a microcontroller, a complexinstruction set computing (CISC) microprocessor, a reduced instructionset (RISC) microprocessor, a very long instruction word (VLIW)microprocessor, or any other type of processing circuit. Furthermore,the processor may refer to one or more individual processors, processingdevices and various elements associated with a processing device thatmay be shared by other processing devices. Optionally, the processor isconfigured to establish communication and networking between thecolumnar database, the ontological database and the search database.

The processor is configured to identify using the ontological database,a set of concepts in each of the plurality of documents and store in thesearch database corresponding to a given document, the set of conceptsidentified in the given document and secondary concepts relating to thegiven document. Notably, a secondary concept has a direct relationshipin the network with at least one of the concepts in the set. In aninstance, the set of concepts identified in a given document may be‘lung cancer’, ‘EGFR’, ‘Tylenol’. The concept ‘lung cancer’ may have asecondary concept relating thereto such as ‘Docetaxel’. Therefore, thesecondary concept ‘Docetaxel’ is also stored with the given document inthe search database.

Optionally, the processor is operable to decouple metadata correspondingto each of the plurality of documents from the plurality of documents,wherein the metadata corresponding to the documents is stored in thesearch database. Notably, in generic search engines the plurality ofdocuments and metadata relating thereto are coupled and stored together.The present disclosure decouples the meta data and the plurality ofdocuments and stores the meta data into the search database and theplurality of documents into the columnar database. Therefore,duplication of data is avoided and leads to storage optimization.Furthermore, in case there is any change in schema of data, whereinschema represents the logical configuration of all or parts of thedatabase, the changed data is not needed to be reindexed again in thesearch database as it can be easily updated on the columnar databaseonly. Hence, maintenance cost is also saved.

The present disclosure also relates to the method as described above.Various embodiments and variants disclosed above apply mutatis mutandisto the method.

The present disclosure further provides a method of retrievingcontext-based information relating to a search query. The methodcomprises receiving the search query. Herein, the term “search query”refers to text provided by a user in order to extract relevantinformation based on context of the search query. Moreover, the relevantinformation may be pertaining to a field of interest of the user.Furthermore, the relevant information may have data related to one ormore keywords of search query included therein. In an example, thesearch query may refer to a particular biomedical entity, for instance,protein, gene, chemical compounds, disorders, deoxyribonucleic acids andribonucleic acids. Herein, the method is implemented using a database,the database comprising the columnar database, the ontological databaseand the search database. The search database is configured to store aninverted index of the plurality of documents in the columnar database,wherein the inverted index stores, corresponding to a given document, aset of concepts identified in the given document, and secondary conceptsrelating to the given document, wherein a secondary concept has a directrelationship in the network with at least one of the concepts in theset.

The method comprises, identifying at least one concept relating to thesearch query, using the ontological database. Notably, one or more wordsor phrases in the search query are compared with the concepts in theontological database to identify at least one concept relating to thesearch query. In an example, the search query may be semanticallyanalyzed to identify nouns present in the search query, wherein theidentified nouns may be used to identify the at least one concept.Optionally, the semantic analysis of the search query may furtherprovide an intent of the user.

The method comprising identifying at least one document relating to thesearch query based on the at least one concept relating to the searchquery, from the columnar database. Notably, using the inverted index ofthe search database, at least one document corresponding to the at leastone concept relating to the search query are identified and suchidentified at least one document is retrieved from the columnar databaseto be provided to the user. It will be appreciated that since theinverted index stores secondary concepts relating to a document in theinverted index, the at least one document may relate to a secondaryconcept.

Optionally, the ontological database is configured to store,corresponding to each of the relationships between the concepts, atleast one attribute relating to the relationship and wherein, in anevent, a plurality of concepts are identified in the search query, themethod comprises identifying at least one document, from the columnardatabase, relating to the search query further based on attributesrelating to the relationships between the plurality of conceptsidentified in the search query. Herein, the at least one attributerelating to a relationship may be a type of the relationship.Specifically, the type of the relationship indicates a nature of therelationship between the two concepts. In an example, the ontologicaldatabase related to biomedical entities. In such example, the conceptsmay be ‘Lung Cancer’ and ‘Docetaxel’. Therefore, the type of therelationship between the two concepts may be ‘cures’, specifically that,‘Lung cancer’ ‘cures’ ‘Docetaxel’. Therefore, the method comprisesidentifying at least one document, from the columnar database, relatingto the search query further based on attributes relating to therelationships to between the plurality of concepts identified in thesearch query. For example, the search query may be analyzed to identifyany words or phrases relating to the type of relationships therein andthereafter, such identified words or phrases may be used to identify oneor more documents based thereon.

Optionally, the ontological database is configured to store,corresponding to each of the relationships between the concepts, aweightage score of the relationship, wherein in an event, a plurality ofconcepts are identified in the search query, the method comprises:

identifying a set of documents from the columnar database, relating tothe search query based on the plurality of concepts relating to thesearch query; and

ranking the documents in the set based on the weightage scores ofrelationships between the plurality of concepts.

Optionally, in this regard, when the plurality of concepts areidentified in a search query, documents having higher weightage scoresbetween their concepts may be ranked higher, when providing results tothe search query. Notably, a higher weightage score of a relationshipbetween two concepts is indicative of a stronger association between thetwo concepts. Furthermore, weightage score of a relationship between twoconcepts may be determined based on a frequency of cooccurrence of thetwo concepts in the plurality of documents. Notably, Breadth FirstSearch (BFS) order is maintained to traverse the tree network topology.In an instance of context-based search, if the search query is ‘generelated to lung cancer’, wherein the biomedical entity term is ‘lungcancer’, but the results needed are of the related gene of ‘lungcancer’. Herein, the inverted index of the ‘lung cancer’ is procured.Furthermore, the nearest gene from the shortest path is realized.Notably, only those set of documents are filtered out which are of therequired gene asked in the search query.

Optionally, since the data is indexed into multiple nodes of the searchdatabase, tree network topology connection between nodes is built whichhelps in optimal serving of the search query and maintaining fasterindexing speed. Additionally, the edges in the ontological databasearising from a particular node can be multiple. However, all the edgesare not equal. The weightage score is given to the edge in theontological database of the present disclosure as per life sciencedomain expert experience data. Furthermore, this weightage scores alongwith other factors decide the most relevant traversal or a method forpath selection. Hence, all the weightage score of the node, documentfrequency decides upon the path to be followed for relevant ranking,relevant realization of relationship based on aggregation andcontext-based search.

Optionally, the method further comprises:

obtaining a list of top concepts from the ontological database; and

ranking the documents in the set based on the list of top concepts.

Optionally, in this regard, the top concepts refer to temporallyfrequently searched concepts. Notably, the list of top conceptsrepresents trending concepts in a given domain in a given time period.Therefore, documents having one or more of the top concepts may beranked higher in the search results.

In an embodiment, the method comprises decoupling metadata correspondingto each of the plurality of documents from the plurality of documents,wherein the metadata corresponding to the documents is stored in thesearch database.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to FIG. 1, there is shown a block diagram of a system 100 forefficient management of a search database 110 for retrievingcontext-based information, in accordance with an embodiment of thepresent disclosure. The system 100 comprises a database 102 and aprocessor 104. The database 102 comprises a columnar database 106 forstoring a plurality of documents, an ontological database 108 configuredto represent a plurality of concepts as nodes in a network andrelationships between the concepts as edges between the nodes; and thesearch database 110 configured to store an inverted index of theplurality of documents in the columnar database 106. The processor 104is configured to identify, using the ontological database 108, a set ofconcepts in each of the plurality of documents and store, in the searchdatabase 110, corresponding to a given document:

the set of concepts identified in the given document; and

secondary concepts relating to the given document, wherein a secondaryconcept has a direct relationship in the network with at least one ofthe concepts in the set.

Referring to FIG. 2, there is shown a flow chart depicting steps of amethod of retrieving context-based information relating to a searchquery, in accordance with an embodiment of the present disclosure. Themethod is implemented using a database comprising a columnar database,an ontological database, and a search database configured to store aninverted index of the plurality of documents in the columnar database,wherein the inverted index stores, corresponding to a given document, aset of concepts identified in the given document, and secondary conceptsrelating to the given document, wherein a secondary concept has a directrelationship in the network with at least one of the concepts in theset. At step 202, the search query is received. At step 204, at leastone concept relating to the search query is identified using theontological database. At step 206, at least one document is identified,from the columnar database, relating to the search query based on the atleast one concept relating to the search query.

Modifications to embodiments of the present disclosure described in theforegoing are possible without departing from the scope of the presentdisclosure as defined by the accompanying claims. Expressions such as“including”, “comprising”, “incorporating”, “have”, “is” used todescribe and claim the present disclosure are intended to be construedin a non-exclusive manner, namely allowing for items, components orelements not explicitly described also to be present. Reference to thesingular is also to be construed to relate to the plural.

1. A system for efficient management of a search database for retrievingcontext-based information, the system comprising a database and aprocessor, wherein the database comprises: a columnar database forstoring a plurality of documents; an ontological database configured torepresent a plurality of concepts as nodes in a network andrelationships between the concepts as edges between the nodes; and thesearch database configured to store an inverted index of the pluralityof documents in the columnar database; wherein the processor isconfigured to identify, using the ontological database, a set ofconcepts in each of the plurality of documents and store, in the searchdatabase, corresponding to a given document: the set of conceptsidentified in the given document; and secondary concepts relating to thegiven document, wherein a secondary concept has a direct relationship inthe network with at least one of the concepts in the set.
 2. A system ofclaim 1, wherein the ontological database is configured to store,corresponding to each of the relationships between the concepts, atleast one attribute relating to the relationship.
 3. A system of claim1, wherein the ontological database is configured to store,corresponding to each of the relationships between the concepts, aweightage score of the relationship.
 4. A system of claim 1, wherein theprocessor is configured to establish communication and networkingbetween the columnar database, the ontological database and the searchdatabase.
 5. A system of claim 1, wherein the processor is operable todecouple metadata corresponding to each of the plurality of documentsfrom the plurality of documents, wherein the metadata corresponding tothe documents is stored in the search database.
 6. A system of claim 1,wherein the ontological database is represented in a tree networktopology as the network.
 7. A method of retrieving context-basedinformation relating to a search query, wherein the method isimplemented using a database, the database comprising: a columnardatabase for storing a plurality of documents, an ontological databaseconfigured to represent a plurality of concepts as nodes in a networkand relationships between the concepts as edges between the nodes, and asearch database configured to store an inverted index of the pluralityof documents in the columnar database, wherein the inverted indexstores, corresponding to a given document, a set of concepts identifiedin the given document, and secondary concepts relating to the givendocument, wherein a secondary concept has a direct relationship in thenetwork with at least one of the concepts in the set; wherein the methodcomprises receiving the search query; identifying at least one conceptrelating to the search query, using the ontological database; andidentifying at least one document, from the columnar database, relatingto the search query based on the at least one concept relating to thesearch query.
 8. A method of claim 7, wherein the ontological databaseis configured to store, corresponding to each of the relationshipsbetween the concepts, at least one attribute relating to therelationship and wherein, in an event, a plurality of concepts areidentified in the search query, the method comprises identifying atleast one document, from the columnar database, relating to the searchquery further based on attributes relating to the relationships betweenthe plurality of concepts identified in the search query.
 9. A method ofclaim 7, wherein the ontological database is configured to store,corresponding to each of the relationships between the concepts, aweightage score of the relationship, wherein in an event, a plurality ofconcepts are identified in the search query, the method comprises:identifying a set of documents from the columnar database, relating tothe search query based on the plurality of concepts relating to thesearch query; and ranking the documents in the set based on theweightage scores of relationships between the plurality of concepts. 10.A method of claim 9, wherein the method further comprises: obtaining alist of top concepts from the ontological database; and ranking thedocuments in the set based on the list of top concepts.
 11. A method ofclaim 7, wherein method comprises decoupling metadata corresponding toeach of the plurality of documents from the plurality of documents,wherein the metadata corresponding to the documents is stored in thesearch database.